Overview

Dataset statistics

Number of variables34
Number of observations11
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory283.6 B

Variable types

Categorical21
Numeric13

Alerts

Country has constant value "BOTSWANA"Constant
Destination Country has constant value "Botswana"Constant
Gross Weight UOM has constant value "KGM"Constant
Net Weight UOM has constant value "KGM"Constant
Month has constant value "August"Constant
Year has constant value "2021"Constant
Hs Code is highly overall correlated with Gross Weight and 9 other fieldsHigh correlation
Gross Weight is highly overall correlated with Hs Code and 17 other fieldsHigh correlation
Net Weight is highly overall correlated with Hs Code and 17 other fieldsHigh correlation
Quantity is highly overall correlated with Gross Weight and 7 other fieldsHigh correlation
No Of Packages is highly overall correlated with Hs Code and 11 other fieldsHigh correlation
Customs Value Bwp is highly overall correlated with Gross Weight and 12 other fieldsHigh correlation
Customs value USD is highly overall correlated with Gross Weight and 12 other fieldsHigh correlation
Invoice Amount BWP is highly overall correlated with Gross Weight and 8 other fieldsHigh correlation
Freight BWP is highly overall correlated with Customs Value Bwp and 6 other fieldsHigh correlation
Vat is highly overall correlated with Gross Weight and 11 other fieldsHigh correlation
Chapter is highly overall correlated with Hs Code and 9 other fieldsHigh correlation
Heading is highly overall correlated with Hs Code and 9 other fieldsHigh correlation
Subheading is highly overall correlated with Hs Code and 9 other fieldsHigh correlation
Date is highly overall correlated with Gross Weight and 7 other fieldsHigh correlation
Importer is highly overall correlated with Hs Code and 26 other fieldsHigh correlation
Importer Address is highly overall correlated with Gross Weight and 11 other fieldsHigh correlation
Exporter is highly overall correlated with Hs Code and 26 other fieldsHigh correlation
Declarant is highly overall correlated with Importer and 10 other fieldsHigh correlation
ORIGIN Origin Country is highly overall correlated with Importer and 9 other fieldsHigh correlation
Origin Country is highly overall correlated with Importer and 9 other fieldsHigh correlation
Export Country is highly overall correlated with Importer and 9 other fieldsHigh correlation
Port Of Entry is highly overall correlated with Customs Value Bwp and 13 other fieldsHigh correlation
Place Of Discharge is highly overall correlated with Customs Value Bwp and 13 other fieldsHigh correlation
Hs Code Description is highly overall correlated with Hs Code and 26 other fieldsHigh correlation
Commercial Description is highly overall correlated with Hs Code and 26 other fieldsHigh correlation
Quantity UOM is highly overall correlated with Importer and 3 other fieldsHigh correlation
Package Type is highly overall correlated with Gross Weight and 10 other fieldsHigh correlation
Declaration Office is highly overall correlated with Customs Value Bwp and 13 other fieldsHigh correlation
Importer is uniformly distributedUniform
Importer Address is uniformly distributedUniform
Exporter is uniformly distributedUniform
Hs Code Description is uniformly distributedUniform
Commercial Description is uniformly distributedUniform
Importer has unique valuesUnique
Exporter has unique valuesUnique
Hs Code has unique valuesUnique
Hs Code Description has unique valuesUnique
Commercial Description has unique valuesUnique
Net Weight has unique valuesUnique
Customs Value Bwp has unique valuesUnique
Customs value USD has unique valuesUnique
Invoice Amount BWP has unique valuesUnique
Freight BWP has unique valuesUnique
Vat has unique valuesUnique
Chapter has unique valuesUnique
Heading has unique valuesUnique
Subheading has unique valuesUnique

Reproduction

Analysis started2023-04-17 14:24:08.014353
Analysis finished2023-04-17 14:24:26.154969
Duration18.14 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

Distinct7
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size216.0 B
11-Aug-21
04-Aug-21
10-Aug-21
01-Aug-21
03-Aug-21
Other values (2)

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters99
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)36.4%

Sample

1st row01-Aug-21
2nd row04-Aug-21
3rd row10-Aug-21
4th row03-Aug-21
5th row11-Aug-21

Common Values

ValueCountFrequency (%)
11-Aug-21 3
27.3%
04-Aug-21 2
18.2%
10-Aug-21 2
18.2%
01-Aug-21 1
 
9.1%
03-Aug-21 1
 
9.1%
05-Aug-21 1
 
9.1%
02-Aug-21 1
 
9.1%

Length

2023-04-17T19:54:26.233099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:26.342543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
11-aug-21 3
27.3%
04-aug-21 2
18.2%
10-aug-21 2
18.2%
01-aug-21 1
 
9.1%
03-aug-21 1
 
9.1%
05-aug-21 1
 
9.1%
02-aug-21 1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
- 22
22.2%
1 20
20.2%
2 12
12.1%
A 11
11.1%
u 11
11.1%
g 11
11.1%
0 8
 
8.1%
4 2
 
2.0%
3 1
 
1.0%
5 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
44.4%
Dash Punctuation 22
22.2%
Lowercase Letter 22
22.2%
Uppercase Letter 11
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20
45.5%
2 12
27.3%
0 8
 
18.2%
4 2
 
4.5%
3 1
 
2.3%
5 1
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
u 11
50.0%
g 11
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
66.7%
Latin 33
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
- 22
33.3%
1 20
30.3%
2 12
18.2%
0 8
 
12.1%
4 2
 
3.0%
3 1
 
1.5%
5 1
 
1.5%
Latin
ValueCountFrequency (%)
A 11
33.3%
u 11
33.3%
g 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 22
22.2%
1 20
20.2%
2 12
12.1%
A 11
11.1%
u 11
11.1%
g 11
11.1%
0 8
 
8.1%
4 2
 
2.0%
3 1
 
1.0%
5 1
 
1.0%

Importer
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.0 B
WOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED
S.R.J. ENTERPRISES (PROPRIETARY) LIMITED
VISITION ROYAL INVESTMENT (PROPRIETARY0 LIMITED
BUILDERS TRADE DEPOT(BOTSWANA)(PTY)LTD
BOTGOOD INVESTMENTS (PRPPRIETARY) LIMITED
Other values (6)

Length

Max length47
Median length41
Mean length38.818182
Min length30

Characters and Unicode

Total characters427
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st rowWOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED
2nd rowS.R.J. ENTERPRISES (PROPRIETARY) LIMITED
3rd rowVISITION ROYAL INVESTMENT (PROPRIETARY0 LIMITED
4th rowBUILDERS TRADE DEPOT(BOTSWANA)(PTY)LTD
5th rowBOTGOOD INVESTMENTS (PRPPRIETARY) LIMITED

Common Values

ValueCountFrequency (%)
WOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED 1
9.1%
S.R.J. ENTERPRISES (PROPRIETARY) LIMITED 1
9.1%
VISITION ROYAL INVESTMENT (PROPRIETARY0 LIMITED 1
9.1%
BUILDERS TRADE DEPOT(BOTSWANA)(PTY)LTD 1
9.1%
BOTGOOD INVESTMENTS (PRPPRIETARY) LIMITED 1
9.1%
PRIMEFAST (PROPRIETARY) LIMITED 1
9.1%
CORDNEX ENTERPRISE (PROPRIETARY) LIMITED 1
9.1%
PRIVYTEC (PROPRIETARY) LIMITED 1
9.1%
LOGICAL DEMAND (PROPRIETARY) LIMITED 1
9.1%
HEART OF AFRICA GIFTS (PROPRIETARY) LIMITED 1
9.1%

Length

2023-04-17T19:54:26.420670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
limited 10
22.7%
proprietary 8
18.2%
investments 2
 
4.5%
woolworths 1
 
2.3%
prpprietary 1
 
2.3%
gifts 1
 
2.3%
africa 1
 
2.3%
of 1
 
2.3%
heart 1
 
2.3%
demand 1
 
2.3%
Other values (17) 17
38.6%

Most occurring characters

ValueCountFrequency (%)
I 45
 
10.5%
R 45
 
10.5%
T 41
 
9.6%
E 41
 
9.6%
33
 
7.7%
P 27
 
6.3%
O 23
 
5.4%
A 22
 
5.2%
D 18
 
4.2%
L 16
 
3.7%
Other values (18) 116
27.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 366
85.7%
Space Separator 33
 
7.7%
Open Punctuation 13
 
3.0%
Close Punctuation 11
 
2.6%
Other Punctuation 3
 
0.7%
Decimal Number 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 45
12.3%
R 45
12.3%
T 41
11.2%
E 41
11.2%
P 27
 
7.4%
O 23
 
6.3%
A 22
 
6.0%
D 18
 
4.9%
L 16
 
4.4%
S 16
 
4.4%
Other values (13) 72
19.7%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 366
85.7%
Common 61
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 45
12.3%
R 45
12.3%
T 41
11.2%
E 41
11.2%
P 27
 
7.4%
O 23
 
6.3%
A 22
 
6.0%
D 18
 
4.9%
L 16
 
4.4%
S 16
 
4.4%
Other values (13) 72
19.7%
Common
ValueCountFrequency (%)
33
54.1%
( 13
 
21.3%
) 11
 
18.0%
. 3
 
4.9%
0 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 45
 
10.5%
R 45
 
10.5%
T 41
 
9.6%
E 41
 
9.6%
33
 
7.7%
P 27
 
6.3%
O 23
 
5.4%
A 22
 
5.2%
D 18
 
4.2%
L 16
 
3.7%
Other values (18) 116
27.2%

Importer Address
Categorical

HIGH CORRELATION  UNIFORM 

Distinct10
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size216.0 B
P O BOX 550296
P O BOX 500017
PO BOX 281
P O BOX 70021
P.O. Box 50131
Other values (5)

Length

Max length17
Median length14
Mean length12.909091
Min length10

Characters and Unicode

Total characters142
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)81.8%

Sample

1st rowP O BOX 500017
2nd rowPO BOX 281
3rd rowP O BOX 550296
4th rowP O BOX 70021
5th rowP.O. Box 50131

Common Values

ValueCountFrequency (%)
P O BOX 550296 2
18.2%
P O BOX 500017 1
9.1%
PO BOX 281 1
9.1%
P O BOX 70021 1
9.1%
P.O. Box 50131 1
9.1%
PRIVATE BAG 1
9.1%
P O BOX 19 1
9.1%
P O BOX AD 75 ACJ 1
9.1%
P O BOX 1612 1
9.1%
P.O. Box 1344 1
9.1%

Length

2023-04-17T19:54:26.514422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:26.608172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
box 10
24.4%
p 7
17.1%
o 7
17.1%
550296 2
 
4.9%
p.o 2
 
4.9%
bag 1
 
2.4%
1612 1
 
2.4%
acj 1
 
2.4%
75 1
 
2.4%
ad 1
 
2.4%
Other values (8) 8
19.5%

Most occurring characters

ValueCountFrequency (%)
30
21.1%
O 18
12.7%
P 11
 
7.7%
B 11
 
7.7%
1 9
 
6.3%
X 8
 
5.6%
0 8
 
5.6%
5 7
 
4.9%
2 5
 
3.5%
A 4
 
2.8%
Other values (18) 31
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 61
43.0%
Decimal Number 43
30.3%
Space Separator 30
21.1%
Other Punctuation 4
 
2.8%
Lowercase Letter 4
 
2.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 18
29.5%
P 11
18.0%
B 11
18.0%
X 8
13.1%
A 4
 
6.6%
R 1
 
1.6%
I 1
 
1.6%
V 1
 
1.6%
T 1
 
1.6%
E 1
 
1.6%
Other values (4) 4
 
6.6%
Decimal Number
ValueCountFrequency (%)
1 9
20.9%
0 8
18.6%
5 7
16.3%
2 5
11.6%
9 3
 
7.0%
6 3
 
7.0%
7 3
 
7.0%
3 2
 
4.7%
4 2
 
4.7%
8 1
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
o 2
50.0%
x 2
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
54.2%
Latin 65
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 18
27.7%
P 11
16.9%
B 11
16.9%
X 8
12.3%
A 4
 
6.2%
o 2
 
3.1%
x 2
 
3.1%
R 1
 
1.5%
I 1
 
1.5%
V 1
 
1.5%
Other values (6) 6
 
9.2%
Common
ValueCountFrequency (%)
30
39.0%
1 9
 
11.7%
0 8
 
10.4%
5 7
 
9.1%
2 5
 
6.5%
. 4
 
5.2%
9 3
 
3.9%
6 3
 
3.9%
7 3
 
3.9%
3 2
 
2.6%
Other values (2) 3
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
21.1%
O 18
12.7%
P 11
 
7.7%
B 11
 
7.7%
1 9
 
6.3%
X 8
 
5.6%
0 8
 
5.6%
5 7
 
4.9%
2 5
 
3.5%
A 4
 
2.8%
Other values (18) 31
21.8%

Exporter
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.0 B
WOOLWORTHS
TRUSEAL
YIWU BORZ E-COMMERCE CO LTD
GYPROC SAINT GTOBAIN
JUDYS PRIDE FASHIONS PTY LTD
Other values (6)

Length

Max length54
Median length20
Mean length22.272727
Min length7

Characters and Unicode

Total characters245
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st rowWOOLWORTHS
2nd rowTRUSEAL
3rd rowYIWU BORZ E-COMMERCE CO LTD
4th rowGYPROC SAINT GTOBAIN
5th rowJUDYS PRIDE FASHIONS PTY LTD

Common Values

ValueCountFrequency (%)
WOOLWORTHS 1
9.1%
TRUSEAL 1
9.1%
YIWU BORZ E-COMMERCE CO LTD 1
9.1%
GYPROC SAINT GTOBAIN 1
9.1%
JUDYS PRIDE FASHIONS PTY LTD 1
9.1%
BENDORA BOERDERY 1
9.1%
NUTRI FEEDS PTY LTD 1
9.1%
E-ENERGY HOLDING LTD 1
9.1%
MAANSHAN YINGKAI INTERNATIONAL TRADING COMPANY LIMITED 1
9.1%
OROAFRICA (PTY)LTD 1
9.1%

Length

2023-04-17T19:54:26.717552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltd 5
 
13.9%
e-commerce 2
 
5.6%
co 2
 
5.6%
pty 2
 
5.6%
woolworths 1
 
2.8%
feeds 1
 
2.8%
pty)ltd 1
 
2.8%
oroafrica 1
 
2.8%
limited 1
 
2.8%
company 1
 
2.8%
Other values (19) 19
52.8%

Most occurring characters

ValueCountFrequency (%)
25
 
10.2%
O 19
 
7.8%
T 18
 
7.3%
E 18
 
7.3%
R 17
 
6.9%
I 16
 
6.5%
A 15
 
6.1%
N 15
 
6.1%
D 14
 
5.7%
L 11
 
4.5%
Other values (17) 77
31.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 215
87.8%
Space Separator 25
 
10.2%
Dash Punctuation 3
 
1.2%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 19
 
8.8%
T 18
 
8.4%
E 18
 
8.4%
R 17
 
7.9%
I 16
 
7.4%
A 15
 
7.0%
N 15
 
7.0%
D 14
 
6.5%
L 11
 
5.1%
Y 11
 
5.1%
Other values (13) 61
28.4%
Space Separator
ValueCountFrequency (%)
25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 215
87.8%
Common 30
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 19
 
8.8%
T 18
 
8.4%
E 18
 
8.4%
R 17
 
7.9%
I 16
 
7.4%
A 15
 
7.0%
N 15
 
7.0%
D 14
 
6.5%
L 11
 
5.1%
Y 11
 
5.1%
Other values (13) 61
28.4%
Common
ValueCountFrequency (%)
25
83.3%
- 3
 
10.0%
( 1
 
3.3%
) 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25
 
10.2%
O 19
 
7.8%
T 18
 
7.3%
E 18
 
7.3%
R 17
 
6.9%
I 16
 
6.5%
A 15
 
6.1%
N 15
 
6.1%
D 14
 
5.7%
L 11
 
4.5%
Other values (17) 77
31.4%

Declarant
Categorical

Distinct9
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size216.0 B
NOTCHABOVE (PROPRIETARY) LIMITED
HYPER TRANSPORT (PROPRIETARY) LIMITED
WOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED
GLOBE-TECH INVESTMENTS (PROPRIETARY) LIMITED
BMR AGENTS (PROPRIETARY) LIMITED
Other values (4)

Length

Max length44
Median length41
Mean length36.818182
Min length31

Characters and Unicode

Total characters405
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st rowWOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED
2nd rowGLOBE-TECH INVESTMENTS (PROPRIETARY) LIMITED
3rd rowNOTCHABOVE (PROPRIETARY) LIMITED
4th rowHYPER TRANSPORT (PROPRIETARY) LIMITED
5th rowBMR AGENTS (PROPRIETARY) LIMITED

Common Values

ValueCountFrequency (%)
NOTCHABOVE (PROPRIETARY) LIMITED 2
18.2%
HYPER TRANSPORT (PROPRIETARY) LIMITED 2
18.2%
WOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITED 1
9.1%
GLOBE-TECH INVESTMENTS (PROPRIETARY) LIMITED 1
9.1%
BMR AGENTS (PROPRIETARY) LIMITED 1
9.1%
TIKULE MARKETING (PROPRIETARY) LIMITED 1
9.1%
ONE NATION FREIGTHS (PROPRIETARY) LIMITED 1
9.1%
LONGPOINT (PROPRIETARY) LIMITED 1
9.1%
PINNACLE EXPRESS (PROPRIETARY) LIMITED 1
9.1%

Length

2023-04-17T19:54:26.780049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:26.889421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
limited 11
26.2%
proprietary 11
26.2%
notchabove 2
 
4.8%
hyper 2
 
4.8%
transport 2
 
4.8%
marketing 1
 
2.4%
pinnacle 1
 
2.4%
longpoint 1
 
2.4%
freigths 1
 
2.4%
nation 1
 
2.4%
Other values (9) 9
21.4%

Most occurring characters

ValueCountFrequency (%)
R 44
10.9%
I 40
 
9.9%
T 39
 
9.6%
E 38
 
9.4%
31
 
7.7%
P 29
 
7.2%
O 26
 
6.4%
A 21
 
5.2%
L 16
 
4.0%
N 16
 
4.0%
Other values (17) 105
25.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 349
86.2%
Space Separator 31
 
7.7%
Open Punctuation 12
 
3.0%
Close Punctuation 12
 
3.0%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 44
12.6%
I 40
11.5%
T 39
11.2%
E 38
10.9%
P 29
8.3%
O 26
 
7.4%
A 21
 
6.0%
L 16
 
4.6%
N 16
 
4.6%
M 14
 
4.0%
Other values (13) 66
18.9%
Space Separator
ValueCountFrequency (%)
31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 349
86.2%
Common 56
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 44
12.6%
I 40
11.5%
T 39
11.2%
E 38
10.9%
P 29
8.3%
O 26
 
7.4%
A 21
 
6.0%
L 16
 
4.6%
N 16
 
4.6%
M 14
 
4.0%
Other values (13) 66
18.9%
Common
ValueCountFrequency (%)
31
55.4%
( 12
 
21.4%
) 12
 
21.4%
- 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 44
10.9%
I 40
 
9.9%
T 39
 
9.6%
E 38
 
9.4%
31
 
7.7%
P 29
 
7.2%
O 26
 
6.4%
A 21
 
5.2%
L 16
 
4.0%
N 16
 
4.0%
Other values (17) 105
25.9%

Country
Categorical

Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
BOTSWANA
11 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters88
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOTSWANA
2nd rowBOTSWANA
3rd rowBOTSWANA
4th rowBOTSWANA
5th rowBOTSWANA

Common Values

ValueCountFrequency (%)
BOTSWANA 11
100.0%

Length

2023-04-17T19:54:27.014344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.092479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
botswana 11
100.0%

Most occurring characters

ValueCountFrequency (%)
A 22
25.0%
B 11
12.5%
O 11
12.5%
T 11
12.5%
S 11
12.5%
W 11
12.5%
N 11
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 88
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 22
25.0%
B 11
12.5%
O 11
12.5%
T 11
12.5%
S 11
12.5%
W 11
12.5%
N 11
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 88
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 22
25.0%
B 11
12.5%
O 11
12.5%
T 11
12.5%
S 11
12.5%
W 11
12.5%
N 11
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 22
25.0%
B 11
12.5%
O 11
12.5%
T 11
12.5%
S 11
12.5%
W 11
12.5%
N 11
12.5%
Distinct3
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size216.0 B
SOUTH AFRICA
CHINA
HONG KONG

Length

Max length12
Median length12
Mean length9.8181818
Min length5

Characters and Unicode

Total characters108
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowSOUTH AFRICA
2nd rowSOUTH AFRICA
3rd rowCHINA
4th rowSOUTH AFRICA
5th rowSOUTH AFRICA

Common Values

ValueCountFrequency (%)
SOUTH AFRICA 7
63.6%
CHINA 3
27.3%
HONG KONG 1
 
9.1%

Length

2023-04-17T19:54:27.170616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.279979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
south 7
36.8%
africa 7
36.8%
china 3
15.8%
hong 1
 
5.3%
kong 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
A 17
15.7%
H 11
10.2%
I 10
9.3%
C 10
9.3%
O 9
8.3%
8
7.4%
S 7
6.5%
U 7
6.5%
T 7
6.5%
F 7
6.5%
Other values (4) 15
13.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100
92.6%
Space Separator 8
 
7.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17
17.0%
H 11
11.0%
I 10
10.0%
C 10
10.0%
O 9
9.0%
S 7
7.0%
U 7
7.0%
T 7
7.0%
F 7
7.0%
R 7
7.0%
Other values (3) 8
8.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100
92.6%
Common 8
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17
17.0%
H 11
11.0%
I 10
10.0%
C 10
10.0%
O 9
9.0%
S 7
7.0%
U 7
7.0%
T 7
7.0%
F 7
7.0%
R 7
7.0%
Other values (3) 8
8.0%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17
15.7%
H 11
10.2%
I 10
9.3%
C 10
9.3%
O 9
8.3%
8
7.4%
S 7
6.5%
U 7
6.5%
T 7
6.5%
F 7
6.5%
Other values (4) 15
13.9%

Origin Country
Categorical

Distinct3
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size216.0 B
South Africa
China
Hong Kong

Length

Max length12
Median length12
Mean length9.8181818
Min length5

Characters and Unicode

Total characters108
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowChina
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa 7
63.6%
China 3
27.3%
Hong Kong 1
 
9.1%

Length

2023-04-17T19:54:27.358103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.436226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
south 7
36.8%
africa 7
36.8%
china 3
15.8%
hong 1
 
5.3%
kong 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a 10
 
9.3%
h 10
 
9.3%
i 10
 
9.3%
o 9
 
8.3%
8
 
7.4%
S 7
 
6.5%
c 7
 
6.5%
r 7
 
6.5%
f 7
 
6.5%
A 7
 
6.5%
Other values (7) 26
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81
75.0%
Uppercase Letter 19
 
17.6%
Space Separator 8
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10
12.3%
h 10
12.3%
i 10
12.3%
o 9
11.1%
c 7
8.6%
r 7
8.6%
f 7
8.6%
t 7
8.6%
u 7
8.6%
n 5
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 7
36.8%
A 7
36.8%
C 3
15.8%
H 1
 
5.3%
K 1
 
5.3%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100
92.6%
Common 8
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10
10.0%
h 10
10.0%
i 10
10.0%
o 9
9.0%
S 7
 
7.0%
c 7
 
7.0%
r 7
 
7.0%
f 7
 
7.0%
A 7
 
7.0%
t 7
 
7.0%
Other values (6) 19
19.0%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10
 
9.3%
h 10
 
9.3%
i 10
 
9.3%
o 9
 
8.3%
8
 
7.4%
S 7
 
6.5%
c 7
 
6.5%
r 7
 
6.5%
f 7
 
6.5%
A 7
 
6.5%
Other values (7) 26
24.1%

Export Country
Categorical

Distinct3
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size216.0 B
South Africa
China
Hong Kong

Length

Max length12
Median length12
Mean length9.8181818
Min length5

Characters and Unicode

Total characters108
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowChina
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa 7
63.6%
China 3
27.3%
Hong Kong 1
 
9.1%

Length

2023-04-17T19:54:27.514349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.592542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
south 7
36.8%
africa 7
36.8%
china 3
15.8%
hong 1
 
5.3%
kong 1
 
5.3%

Most occurring characters

ValueCountFrequency (%)
a 10
 
9.3%
h 10
 
9.3%
i 10
 
9.3%
o 9
 
8.3%
8
 
7.4%
S 7
 
6.5%
c 7
 
6.5%
r 7
 
6.5%
f 7
 
6.5%
A 7
 
6.5%
Other values (7) 26
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81
75.0%
Uppercase Letter 19
 
17.6%
Space Separator 8
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10
12.3%
h 10
12.3%
i 10
12.3%
o 9
11.1%
c 7
8.6%
r 7
8.6%
f 7
8.6%
t 7
8.6%
u 7
8.6%
n 5
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 7
36.8%
A 7
36.8%
C 3
15.8%
H 1
 
5.3%
K 1
 
5.3%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100
92.6%
Common 8
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10
10.0%
h 10
10.0%
i 10
10.0%
o 9
9.0%
S 7
 
7.0%
c 7
 
7.0%
r 7
 
7.0%
f 7
 
7.0%
A 7
 
7.0%
t 7
 
7.0%
Other values (6) 19
19.0%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10
 
9.3%
h 10
 
9.3%
i 10
 
9.3%
o 9
 
8.3%
8
 
7.4%
S 7
 
6.5%
c 7
 
6.5%
r 7
 
6.5%
f 7
 
6.5%
A 7
 
6.5%
Other values (7) 26
24.1%
Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
Botswana
11 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters88
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBotswana
2nd rowBotswana
3rd rowBotswana
4th rowBotswana
5th rowBotswana

Common Values

ValueCountFrequency (%)
Botswana 11
100.0%

Length

2023-04-17T19:54:27.655054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.733168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
botswana 11
100.0%

Most occurring characters

ValueCountFrequency (%)
a 22
25.0%
B 11
12.5%
o 11
12.5%
t 11
12.5%
s 11
12.5%
w 11
12.5%
n 11
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77
87.5%
Uppercase Letter 11
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22
28.6%
o 11
14.3%
t 11
14.3%
s 11
14.3%
w 11
14.3%
n 11
14.3%
Uppercase Letter
ValueCountFrequency (%)
B 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22
25.0%
B 11
12.5%
o 11
12.5%
t 11
12.5%
s 11
12.5%
w 11
12.5%
n 11
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22
25.0%
B 11
12.5%
o 11
12.5%
t 11
12.5%
s 11
12.5%
w 11
12.5%
n 11
12.5%

Port Of Entry
Categorical

Distinct5
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size216.0 B
Tlokweng Gate
GABCON
Ramatlabama Borderpost
Pioneer Gate
Sir Seretse Khama Airport

Length

Max length25
Median length22
Mean length13.636364
Min length6

Characters and Unicode

Total characters150
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowTlokweng Gate
2nd rowTlokweng Gate
3rd rowGABCON
4th rowRamatlabama Borderpost
5th rowTlokweng Gate

Common Values

ValueCountFrequency (%)
Tlokweng Gate 3
27.3%
GABCON 3
27.3%
Ramatlabama Borderpost 2
18.2%
Pioneer Gate 2
18.2%
Sir Seretse Khama Airport 1
 
9.1%

Length

2023-04-17T19:54:27.795675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:27.889425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gate 5
23.8%
tlokweng 3
14.3%
gabcon 3
14.3%
ramatlabama 2
 
9.5%
borderpost 2
 
9.5%
pioneer 2
 
9.5%
sir 1
 
4.8%
seretse 1
 
4.8%
khama 1
 
4.8%
airport 1
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 17
 
11.3%
a 17
 
11.3%
t 11
 
7.3%
o 10
 
6.7%
10
 
6.7%
r 10
 
6.7%
G 8
 
5.3%
m 5
 
3.3%
n 5
 
3.3%
B 5
 
3.3%
Other values (19) 52
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104
69.3%
Uppercase Letter 36
 
24.0%
Space Separator 10
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17
16.3%
a 17
16.3%
t 11
10.6%
o 10
9.6%
r 10
9.6%
m 5
 
4.8%
n 5
 
4.8%
l 5
 
4.8%
i 4
 
3.8%
s 3
 
2.9%
Other values (7) 17
16.3%
Uppercase Letter
ValueCountFrequency (%)
G 8
22.2%
B 5
13.9%
A 4
11.1%
T 3
 
8.3%
C 3
 
8.3%
N 3
 
8.3%
O 3
 
8.3%
R 2
 
5.6%
P 2
 
5.6%
S 2
 
5.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140
93.3%
Common 10
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17
 
12.1%
a 17
 
12.1%
t 11
 
7.9%
o 10
 
7.1%
r 10
 
7.1%
G 8
 
5.7%
m 5
 
3.6%
n 5
 
3.6%
B 5
 
3.6%
l 5
 
3.6%
Other values (18) 47
33.6%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17
 
11.3%
a 17
 
11.3%
t 11
 
7.3%
o 10
 
6.7%
10
 
6.7%
r 10
 
6.7%
G 8
 
5.3%
m 5
 
3.3%
n 5
 
3.3%
B 5
 
3.3%
Other values (19) 52
34.7%
Distinct5
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size216.0 B
Tlokweng Gate
GABCON
Ramatlabama Borderpost
Pioneer Gate
Sir Seretse Khama Airport

Length

Max length25
Median length22
Mean length13.636364
Min length6

Characters and Unicode

Total characters150
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowTlokweng Gate
2nd rowTlokweng Gate
3rd rowGABCON
4th rowRamatlabama Borderpost
5th rowTlokweng Gate

Common Values

ValueCountFrequency (%)
Tlokweng Gate 3
27.3%
GABCON 3
27.3%
Ramatlabama Borderpost 2
18.2%
Pioneer Gate 2
18.2%
Sir Seretse Khama Airport 1
 
9.1%

Length

2023-04-17T19:54:27.967474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:28.076924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gate 5
23.8%
tlokweng 3
14.3%
gabcon 3
14.3%
ramatlabama 2
 
9.5%
borderpost 2
 
9.5%
pioneer 2
 
9.5%
sir 1
 
4.8%
seretse 1
 
4.8%
khama 1
 
4.8%
airport 1
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 17
 
11.3%
a 17
 
11.3%
t 11
 
7.3%
o 10
 
6.7%
10
 
6.7%
r 10
 
6.7%
G 8
 
5.3%
m 5
 
3.3%
n 5
 
3.3%
B 5
 
3.3%
Other values (19) 52
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104
69.3%
Uppercase Letter 36
 
24.0%
Space Separator 10
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17
16.3%
a 17
16.3%
t 11
10.6%
o 10
9.6%
r 10
9.6%
m 5
 
4.8%
n 5
 
4.8%
l 5
 
4.8%
i 4
 
3.8%
s 3
 
2.9%
Other values (7) 17
16.3%
Uppercase Letter
ValueCountFrequency (%)
G 8
22.2%
B 5
13.9%
A 4
11.1%
T 3
 
8.3%
C 3
 
8.3%
N 3
 
8.3%
O 3
 
8.3%
R 2
 
5.6%
P 2
 
5.6%
S 2
 
5.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140
93.3%
Common 10
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17
 
12.1%
a 17
 
12.1%
t 11
 
7.9%
o 10
 
7.1%
r 10
 
7.1%
G 8
 
5.7%
m 5
 
3.6%
n 5
 
3.6%
B 5
 
3.6%
l 5
 
3.6%
Other values (18) 47
33.6%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17
 
11.3%
a 17
 
11.3%
t 11
 
7.3%
o 10
 
6.7%
10
 
6.7%
r 10
 
6.7%
G 8
 
5.3%
m 5
 
3.3%
n 5
 
3.3%
B 5
 
3.3%
Other values (19) 52
34.7%

Hs Code
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53017947
Minimum3063900
Maximum96085000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:28.155057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3063900
5-th percentile7596950
Q131184091
median44189900
Q384989510
95-th percentile91584002
Maximum96085000
Range93021100
Interquartile range (IQR)53805419

Descriptive statistics

Standard deviation32836735
Coefficient of variation (CV)0.61935131
Kurtosis-1.5140027
Mean53017947
Median Absolute Deviation (MAD)32059900
Skewness-0.12759858
Sum5.8319742 × 108
Variance1.0782511 × 1015
MonotonicityNot monotonic
2023-04-17T19:54:28.233175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3063900 1
9.1%
40169310 1
9.1%
84688000 1
9.1%
44189900 1
9.1%
68129100 1
9.1%
12130000 1
9.1%
23099092 1
9.1%
85291020 1
9.1%
39269090 1
9.1%
96085000 1
9.1%
ValueCountFrequency (%)
3063900 1
9.1%
12130000 1
9.1%
23099092 1
9.1%
39269090 1
9.1%
40169310 1
9.1%
44189900 1
9.1%
68129100 1
9.1%
84688000 1
9.1%
85291020 1
9.1%
87083003 1
9.1%
ValueCountFrequency (%)
96085000 1
9.1%
87083003 1
9.1%
85291020 1
9.1%
84688000 1
9.1%
68129100 1
9.1%
44189900 1
9.1%
40169310 1
9.1%
39269090 1
9.1%
23099092 1
9.1%
12130000 1
9.1%

Hs Code Description
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.0 B
Other, including flour, meals and pellets of crustaceans fit for human
Identifiable as integral parts of industrial machinery
Other machinery and apparatus
Other builders' joinery and carpentry of wood, including cellular wood
Clothing, clothing accessories, footwear and headgear
Other values (6)

Length

Max length77
Median length62
Mean length57.545455
Min length24

Characters and Unicode

Total characters633
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st rowOther, including flour, meals and pellets of crustaceans fit for human
2nd rowIdentifiable as integral parts of industrial machinery
3rd rowOther machinery and apparatus
4th rowOther builders' joinery and carpentry of wood, including cellular wood
5th rowClothing, clothing accessories, footwear and headgear

Common Values

ValueCountFrequency (%)
Other, including flour, meals and pellets of crustaceans fit for human 1
9.1%
Identifiable as integral parts of industrial machinery 1
9.1%
Other machinery and apparatus 1
9.1%
Other builders' joinery and carpentry of wood, including cellular wood 1
9.1%
Clothing, clothing accessories, footwear and headgear 1
9.1%
Cereal straw and husks, unprepared, whether or not chopped, ground, 1
9.1%
Other Preparations of a kind used in animal feeding 1
9.1%
Other aerials for reception apparatus for television, whether or not capable 1
9.1%
Other articles of plastics and articles of other materials of headings .39.01 1
9.1%
Sets of articles from two or more of the foregoing subheadings 1
9.1%

Length

2023-04-17T19:54:28.311294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 9
 
9.5%
other 7
 
7.4%
and 6
 
6.3%
articles 3
 
3.2%
or 3
 
3.2%
for 3
 
3.2%
machinery 2
 
2.1%
not 2
 
2.1%
including 2
 
2.1%
clothing 2
 
2.1%
Other values (53) 56
58.9%

Most occurring characters

ValueCountFrequency (%)
84
13.3%
e 56
 
8.8%
a 51
 
8.1%
r 49
 
7.7%
o 40
 
6.3%
t 37
 
5.8%
n 36
 
5.7%
i 35
 
5.5%
s 32
 
5.1%
l 25
 
3.9%
Other values (27) 188
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 519
82.0%
Space Separator 84
 
13.3%
Other Punctuation 14
 
2.2%
Uppercase Letter 12
 
1.9%
Decimal Number 4
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56
10.8%
a 51
 
9.8%
r 49
 
9.4%
o 40
 
7.7%
t 37
 
7.1%
n 36
 
6.9%
i 35
 
6.7%
s 32
 
6.2%
l 25
 
4.8%
d 24
 
4.6%
Other values (13) 134
25.8%
Uppercase Letter
ValueCountFrequency (%)
O 6
50.0%
C 2
 
16.7%
I 1
 
8.3%
P 1
 
8.3%
S 1
 
8.3%
D 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
3 1
25.0%
9 1
25.0%
0 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 11
78.6%
. 2
 
14.3%
' 1
 
7.1%
Space Separator
ValueCountFrequency (%)
84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 531
83.9%
Common 102
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56
 
10.5%
a 51
 
9.6%
r 49
 
9.2%
o 40
 
7.5%
t 37
 
7.0%
n 36
 
6.8%
i 35
 
6.6%
s 32
 
6.0%
l 25
 
4.7%
d 24
 
4.5%
Other values (19) 146
27.5%
Common
ValueCountFrequency (%)
84
82.4%
, 11
 
10.8%
. 2
 
2.0%
' 1
 
1.0%
3 1
 
1.0%
9 1
 
1.0%
0 1
 
1.0%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
13.3%
e 56
 
8.8%
a 51
 
8.1%
r 49
 
7.7%
o 40
 
6.3%
t 37
 
5.8%
n 36
 
5.7%
i 35
 
5.5%
s 32
 
5.1%
l 25
 
3.9%
Other values (27) 188
29.7%

Commercial Description
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.0 B
Other, including flours, meals and pellets of crustaceans, fit for human
SEAL
MINI ELECTRIC WELDING MACHINE
CORNICE
OTHER
Other values (6)

Length

Max length72
Median length21
Mean length23.454545
Min length4

Characters and Unicode

Total characters258
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)100.0%

Sample

1st rowOther, including flours, meals and pellets of crustaceans, fit for human
2nd rowSEAL
3rd rowMINI ELECTRIC WELDING MACHINE
4th rowCORNICE
5th rowOTHER

Common Values

ValueCountFrequency (%)
Other, including flours, meals and pellets of crustaceans, fit for human 1
9.1%
SEAL 1
9.1%
MINI ELECTRIC WELDING MACHINE 1
9.1%
CORNICE 1
9.1%
OTHER 1
9.1%
WHEAT STRAW 1
9.1%
TUB ENERGY,PROTEIN ENERY AND TUB PHOSPHATE. NO BOBS REQUIRED 1
9.1%
MI BOX S EU 1
9.1%
FRESHNESS PROTECTION PACKAGE 1
9.1%
GIFTS SETS 1
9.1%

Length

2023-04-17T19:54:28.389420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other 2
 
4.9%
and 2
 
4.9%
tub 2
 
4.9%
meals 1
 
2.4%
eu 1
 
2.4%
no 1
 
2.4%
bobs 1
 
2.4%
required 1
 
2.4%
mi 1
 
2.4%
box 1
 
2.4%
Other values (28) 28
68.3%

Most occurring characters

ValueCountFrequency (%)
30
 
11.6%
E 24
 
9.3%
S 13
 
5.0%
R 13
 
5.0%
T 12
 
4.7%
I 12
 
4.7%
N 11
 
4.3%
A 11
 
4.3%
O 10
 
3.9%
C 8
 
3.1%
Other values (33) 114
44.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 164
63.6%
Lowercase Letter 58
 
22.5%
Space Separator 30
 
11.6%
Other Punctuation 6
 
2.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 24
14.6%
S 13
 
7.9%
R 13
 
7.9%
T 12
 
7.3%
I 12
 
7.3%
N 11
 
6.7%
A 11
 
6.7%
O 10
 
6.1%
C 8
 
4.9%
B 7
 
4.3%
Other values (13) 43
26.2%
Lowercase Letter
ValueCountFrequency (%)
a 5
 
8.6%
e 5
 
8.6%
n 5
 
8.6%
l 5
 
8.6%
s 5
 
8.6%
t 4
 
6.9%
f 4
 
6.9%
u 4
 
6.9%
r 4
 
6.9%
c 3
 
5.2%
Other values (7) 14
24.1%
Other Punctuation
ValueCountFrequency (%)
, 5
83.3%
. 1
 
16.7%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222
86.0%
Common 36
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 24
 
10.8%
S 13
 
5.9%
R 13
 
5.9%
T 12
 
5.4%
I 12
 
5.4%
N 11
 
5.0%
A 11
 
5.0%
O 10
 
4.5%
C 8
 
3.6%
B 7
 
3.2%
Other values (30) 101
45.5%
Common
ValueCountFrequency (%)
30
83.3%
, 5
 
13.9%
. 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
 
11.6%
E 24
 
9.3%
S 13
 
5.0%
R 13
 
5.0%
T 12
 
4.7%
I 12
 
4.7%
N 11
 
4.3%
A 11
 
4.3%
O 10
 
3.9%
C 8
 
3.1%
Other values (33) 114
44.2%

Gross Weight
Real number (ℝ)

Distinct10
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5483.3909
Minimum0.04
Maximum34000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:28.467488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile1.52
Q19.88
median40.08
Q33245.71
95-th percentile26805.5
Maximum34000
Range33999.96
Interquartile range (IQR)3235.83

Descriptive statistics

Standard deviation11140.933
Coefficient of variation (CV)2.0317597
Kurtosis4.2554415
Mean5483.3909
Median Absolute Deviation (MAD)40.04
Skewness2.1904773
Sum60317.3
Variance1.2412039 × 108
MonotonicityNot monotonic
2023-04-17T19:54:28.592476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 2
18.2%
16.76 1
9.1%
20 1
9.1%
5511.42 1
9.1%
40.08 1
9.1%
19611 1
9.1%
34000 1
9.1%
132 1
9.1%
0.04 1
9.1%
980 1
9.1%
ValueCountFrequency (%)
0.04 1
9.1%
3 2
18.2%
16.76 1
9.1%
20 1
9.1%
40.08 1
9.1%
132 1
9.1%
980 1
9.1%
5511.42 1
9.1%
19611 1
9.1%
34000 1
9.1%
ValueCountFrequency (%)
34000 1
9.1%
19611 1
9.1%
5511.42 1
9.1%
980 1
9.1%
132 1
9.1%
40.08 1
9.1%
20 1
9.1%
16.76 1
9.1%
3 2
18.2%
0.04 1
9.1%

Gross Weight UOM
Categorical

Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
KGM
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKGM
2nd rowKGM
3rd rowKGM
4th rowKGM
5th rowKGM

Common Values

ValueCountFrequency (%)
KGM 11
100.0%

Length

2023-04-17T19:54:28.827257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:28.889755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kgm 11
100.0%

Most occurring characters

ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 33
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Net Weight
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5483.1809
Minimum0.04
Maximum34000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:28.936632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile1.365
Q19.88
median40.08
Q33245.21
95-th percentile26805
Maximum34000
Range33999.96
Interquartile range (IQR)3235.33

Descriptive statistics

Standard deviation11140.821
Coefficient of variation (CV)2.0318172
Kurtosis4.2558956
Mean5483.1809
Median Absolute Deviation (MAD)40.04
Skewness2.1905569
Sum60314.99
Variance1.241179 × 108
MonotonicityNot monotonic
2023-04-17T19:54:29.014772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
16.76 1
9.1%
20 1
9.1%
3 1
9.1%
5510.42 1
9.1%
40.08 1
9.1%
19610 1
9.1%
34000 1
9.1%
2.69 1
9.1%
132 1
9.1%
0.04 1
9.1%
ValueCountFrequency (%)
0.04 1
9.1%
2.69 1
9.1%
3 1
9.1%
16.76 1
9.1%
20 1
9.1%
40.08 1
9.1%
132 1
9.1%
980 1
9.1%
5510.42 1
9.1%
19610 1
9.1%
ValueCountFrequency (%)
34000 1
9.1%
19610 1
9.1%
5510.42 1
9.1%
980 1
9.1%
132 1
9.1%
40.08 1
9.1%
20 1
9.1%
16.76 1
9.1%
3 1
9.1%
2.69 1
9.1%

Net Weight UOM
Categorical

Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
KGM
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKGM
2nd rowKGM
3rd rowKGM
4th rowKGM
5th rowKGM

Common Values

ValueCountFrequency (%)
KGM 11
100.0%

Length

2023-04-17T19:54:29.092960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:29.155457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kgm 11
100.0%

Most occurring characters

ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 33
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 11
33.3%
G 11
33.3%
M 11
33.3%

Quantity
Real number (ℝ)

Distinct10
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4211.2273
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:29.202340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.5
Q120
median132
Q33095.21
95-th percentile19805
Maximum20000
Range19999
Interquartile range (IQR)3075.21

Descriptive statistics

Standard deviation7876.6146
Coefficient of variation (CV)1.8703846
Kurtosis1.5768296
Mean4211.2273
Median Absolute Deviation (MAD)131
Skewness1.7630623
Sum46323.5
Variance62041057
MonotonicityNot monotonic
2023-04-17T19:54:29.264836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 2
18.2%
30 1
9.1%
20000 1
9.1%
1 1
9.1%
5510.42 1
9.1%
40.08 1
9.1%
19610 1
9.1%
680 1
9.1%
132 1
9.1%
300 1
9.1%
ValueCountFrequency (%)
1 1
9.1%
10 2
18.2%
30 1
9.1%
40.08 1
9.1%
132 1
9.1%
300 1
9.1%
680 1
9.1%
5510.42 1
9.1%
19610 1
9.1%
20000 1
9.1%
ValueCountFrequency (%)
20000 1
9.1%
19610 1
9.1%
5510.42 1
9.1%
680 1
9.1%
300 1
9.1%
132 1
9.1%
40.08 1
9.1%
30 1
9.1%
10 2
18.2%
1 1
9.1%

Quantity UOM
Categorical

Distinct2
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size216.0 B
KGM
EA

Length

Max length3
Median length3
Mean length2.8181818
Min length2

Characters and Unicode

Total characters31
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKGM
2nd rowKGM
3rd rowEA
4th rowKGM
5th rowKGM

Common Values

ValueCountFrequency (%)
KGM 9
81.8%
EA 2
 
18.2%

Length

2023-04-17T19:54:29.342964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:29.421081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kgm 9
81.8%
ea 2
 
18.2%

Most occurring characters

ValueCountFrequency (%)
K 9
29.0%
G 9
29.0%
M 9
29.0%
E 2
 
6.5%
A 2
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 9
29.0%
G 9
29.0%
M 9
29.0%
E 2
 
6.5%
A 2
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 31
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 9
29.0%
G 9
29.0%
M 9
29.0%
E 2
 
6.5%
A 2
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 9
29.0%
G 9
29.0%
M 9
29.0%
E 2
 
6.5%
A 2
 
6.5%

No Of Packages
Real number (ℝ)

Distinct9
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1894
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:29.483580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median17
Q341
95-th percentile10340
Maximum20000
Range19999
Interquartile range (IQR)31

Descriptive statistics

Standard deviation6008.3897
Coefficient of variation (CV)3.1723282
Kurtosis10.967523
Mean1894
Median Absolute Deviation (MAD)13
Skewness3.3100497
Sum20834
Variance36100747
MonotonicityNot monotonic
2023-04-17T19:54:29.546079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 2
18.2%
10 2
18.2%
30 1
9.1%
20000 1
9.1%
13 1
9.1%
17 1
9.1%
52 1
9.1%
680 1
9.1%
20 1
9.1%
ValueCountFrequency (%)
1 2
18.2%
10 2
18.2%
13 1
9.1%
17 1
9.1%
20 1
9.1%
30 1
9.1%
52 1
9.1%
680 1
9.1%
20000 1
9.1%
ValueCountFrequency (%)
20000 1
9.1%
680 1
9.1%
52 1
9.1%
30 1
9.1%
20 1
9.1%
17 1
9.1%
13 1
9.1%
10 2
18.2%
1 2
18.2%

Package Type
Categorical

Distinct5
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size216.0 B
EACH
CARTONS
KEG
BALE,COMPRESSED
BAG

Length

Max length15
Median length7
Mean length5.9090909
Min length3

Characters and Unicode

Total characters65
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)27.3%

Sample

1st rowEACH
2nd rowKEG
3rd rowCARTONS
4th rowEACH
5th rowCARTONS

Common Values

ValueCountFrequency (%)
EACH 4
36.4%
CARTONS 4
36.4%
KEG 1
 
9.1%
BALE,COMPRESSED 1
 
9.1%
BAG 1
 
9.1%

Length

2023-04-17T19:54:29.624206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:29.702331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
each 4
36.4%
cartons 4
36.4%
keg 1
 
9.1%
bale,compressed 1
 
9.1%
bag 1
 
9.1%

Most occurring characters

ValueCountFrequency (%)
A 10
15.4%
C 9
13.8%
E 8
12.3%
S 6
9.2%
R 5
7.7%
O 5
7.7%
T 4
 
6.2%
N 4
 
6.2%
H 4
 
6.2%
G 2
 
3.1%
Other values (7) 8
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 64
98.5%
Other Punctuation 1
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10
15.6%
C 9
14.1%
E 8
12.5%
S 6
9.4%
R 5
7.8%
O 5
7.8%
T 4
 
6.2%
N 4
 
6.2%
H 4
 
6.2%
G 2
 
3.1%
Other values (6) 7
10.9%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64
98.5%
Common 1
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10
15.6%
C 9
14.1%
E 8
12.5%
S 6
9.4%
R 5
7.8%
O 5
7.8%
T 4
 
6.2%
N 4
 
6.2%
H 4
 
6.2%
G 2
 
3.1%
Other values (6) 7
10.9%
Common
ValueCountFrequency (%)
, 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 10
15.4%
C 9
13.8%
E 8
12.3%
S 6
9.2%
R 5
7.7%
O 5
7.7%
T 4
 
6.2%
N 4
 
6.2%
H 4
 
6.2%
G 2
 
3.1%
Other values (7) 8
12.3%

Customs Value Bwp
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18773.648
Minimum46.04
Maximum123099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:29.764831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46.04
5-th percentile260.325
Q12017.52
median5176.2
Q317060.025
95-th percentile77283.5
Maximum123099
Range123052.96
Interquartile range (IQR)15042.505

Descriptive statistics

Standard deviation36215.27
Coefficient of variation (CV)1.9290481
Kurtosis8.519861
Mean18773.648
Median Absolute Deviation (MAD)3367.28
Skewness2.8440034
Sum206510.13
Variance1.3115457 × 109
MonotonicityNot monotonic
2023-04-17T19:54:29.842956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1808.92 1
9.1%
2370.89 1
9.1%
46.04 1
9.1%
31468 1
9.1%
7233.35 1
9.1%
26886.7 1
9.1%
123099 1
9.1%
5176.2 1
9.1%
474.61 1
9.1%
5720.3 1
9.1%
ValueCountFrequency (%)
46.04 1
9.1%
474.61 1
9.1%
1808.92 1
9.1%
2226.12 1
9.1%
2370.89 1
9.1%
5176.2 1
9.1%
5720.3 1
9.1%
7233.35 1
9.1%
26886.7 1
9.1%
31468 1
9.1%
ValueCountFrequency (%)
123099 1
9.1%
31468 1
9.1%
26886.7 1
9.1%
7233.35 1
9.1%
5720.3 1
9.1%
5176.2 1
9.1%
2370.89 1
9.1%
2226.12 1
9.1%
1808.92 1
9.1%
474.61 1
9.1%

Customs value USD
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1698.4982
Minimum4.17
Maximum11137.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:29.905455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.17
5-th percentile23.555
Q1182.53
median468.3
Q31543.46
95-th percentile6992.03
Maximum11137.07
Range11132.9
Interquartile range (IQR)1360.93

Descriptive statistics

Standard deviation3276.4845
Coefficient of variation (CV)1.929048
Kurtosis8.5198647
Mean1698.4982
Median Absolute Deviation (MAD)304.64
Skewness2.8440042
Sum18683.48
Variance10735351
MonotonicityNot monotonic
2023-04-17T19:54:29.967959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
163.66 1
9.1%
214.5 1
9.1%
4.17 1
9.1%
2846.99 1
9.1%
654.42 1
9.1%
2432.5 1
9.1%
11137.07 1
9.1%
468.3 1
9.1%
42.94 1
9.1%
517.53 1
9.1%
ValueCountFrequency (%)
4.17 1
9.1%
42.94 1
9.1%
163.66 1
9.1%
201.4 1
9.1%
214.5 1
9.1%
468.3 1
9.1%
517.53 1
9.1%
654.42 1
9.1%
2432.5 1
9.1%
2846.99 1
9.1%
ValueCountFrequency (%)
11137.07 1
9.1%
2846.99 1
9.1%
2432.5 1
9.1%
654.42 1
9.1%
517.53 1
9.1%
468.3 1
9.1%
214.5 1
9.1%
201.4 1
9.1%
163.66 1
9.1%
42.94 1
9.1%
Distinct5
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size216.0 B
Tlokweng Gate
GABCON
Ramatlabama Borderpost
Pioneer Gate
Sir Seretse Khama

Length

Max length22
Median length17
Mean length12.909091
Min length6

Characters and Unicode

Total characters142
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)9.1%

Sample

1st rowTlokweng Gate
2nd rowTlokweng Gate
3rd rowGABCON
4th rowRamatlabama Borderpost
5th rowTlokweng Gate

Common Values

ValueCountFrequency (%)
Tlokweng Gate 3
27.3%
GABCON 3
27.3%
Ramatlabama Borderpost 2
18.2%
Pioneer Gate 2
18.2%
Sir Seretse Khama 1
 
9.1%

Length

2023-04-17T19:54:30.046081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:30.139830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gate 5
25.0%
tlokweng 3
15.0%
gabcon 3
15.0%
ramatlabama 2
 
10.0%
borderpost 2
 
10.0%
pioneer 2
 
10.0%
sir 1
 
5.0%
seretse 1
 
5.0%
khama 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
e 17
 
12.0%
a 17
 
12.0%
t 10
 
7.0%
o 9
 
6.3%
9
 
6.3%
r 8
 
5.6%
G 8
 
5.6%
n 5
 
3.5%
m 5
 
3.5%
B 5
 
3.5%
Other values (19) 49
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98
69.0%
Uppercase Letter 35
 
24.6%
Space Separator 9
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17
17.3%
a 17
17.3%
t 10
10.2%
o 9
9.2%
r 8
8.2%
n 5
 
5.1%
m 5
 
5.1%
l 5
 
5.1%
i 3
 
3.1%
s 3
 
3.1%
Other values (7) 16
16.3%
Uppercase Letter
ValueCountFrequency (%)
G 8
22.9%
B 5
14.3%
T 3
 
8.6%
N 3
 
8.6%
C 3
 
8.6%
O 3
 
8.6%
A 3
 
8.6%
R 2
 
5.7%
P 2
 
5.7%
S 2
 
5.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 133
93.7%
Common 9
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17
 
12.8%
a 17
 
12.8%
t 10
 
7.5%
o 9
 
6.8%
r 8
 
6.0%
G 8
 
6.0%
n 5
 
3.8%
m 5
 
3.8%
B 5
 
3.8%
l 5
 
3.8%
Other values (18) 44
33.1%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17
 
12.0%
a 17
 
12.0%
t 10
 
7.0%
o 9
 
6.3%
9
 
6.3%
r 8
 
5.6%
G 8
 
5.6%
n 5
 
3.5%
m 5
 
3.5%
B 5
 
3.5%
Other values (19) 49
34.5%

Invoice Amount BWP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67771.191
Minimum2370.89
Maximum158701.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.202332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2370.89
5-th percentile3773.545
Q16597.05
median64056.13
Q3121122.31
95-th percentile140900.43
Maximum158701.64
Range156330.75
Interquartile range (IQR)114525.26

Descriptive statistics

Standard deviation60128.969
Coefficient of variation (CV)0.88723495
Kurtosis-1.9004183
Mean67771.191
Median Absolute Deviation (MAD)58095.38
Skewness0.1395394
Sum745483.1
Variance3.6154929 × 109
MonotonicityNot monotonic
2023-04-17T19:54:30.280385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
109753.6 1
9.1%
2370.89 1
9.1%
122562.93 1
9.1%
158701.64 1
9.1%
7233.35 1
9.1%
26886.71 1
9.1%
123099.21 1
9.1%
5176.2 1
9.1%
64056.13 1
9.1%
5960.75 1
9.1%
ValueCountFrequency (%)
2370.89 1
9.1%
5176.2 1
9.1%
5960.75 1
9.1%
7233.35 1
9.1%
26886.71 1
9.1%
64056.13 1
9.1%
109753.6 1
9.1%
119681.69 1
9.1%
122562.93 1
9.1%
123099.21 1
9.1%
ValueCountFrequency (%)
158701.64 1
9.1%
123099.21 1
9.1%
122562.93 1
9.1%
119681.69 1
9.1%
109753.6 1
9.1%
64056.13 1
9.1%
26886.71 1
9.1%
7233.35 1
9.1%
5960.75 1
9.1%
5176.2 1
9.1%

Freight BWP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27273.927
Minimum113.67
Maximum104123.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.342886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum113.67
5-th percentile116.105
Q1853
median6154.96
Q346571.435
95-th percentile93577.435
Maximum104123.21
Range104009.54
Interquartile range (IQR)45718.435

Descriptive statistics

Standard deviation37900.902
Coefficient of variation (CV)1.3896386
Kurtosis0.13374492
Mean27273.927
Median Absolute Deviation (MAD)6036.42
Skewness1.2389952
Sum300013.2
Variance1.4364783 × 109
MonotonicityNot monotonic
2023-04-17T19:54:30.405384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
33767.55 1
9.1%
118.54 1
9.1%
59375.32 1
9.1%
10190.57 1
9.1%
361.67 1
9.1%
1344.33 1
9.1%
6154.96 1
9.1%
1431.72 1
9.1%
104123.21 1
9.1%
113.67 1
9.1%
ValueCountFrequency (%)
113.67 1
9.1%
118.54 1
9.1%
361.67 1
9.1%
1344.33 1
9.1%
1431.72 1
9.1%
6154.96 1
9.1%
10190.57 1
9.1%
33767.55 1
9.1%
59375.32 1
9.1%
83031.66 1
9.1%
ValueCountFrequency (%)
104123.21 1
9.1%
83031.66 1
9.1%
59375.32 1
9.1%
33767.55 1
9.1%
10190.57 1
9.1%
6154.96 1
9.1%
1431.72 1
9.1%
1344.33 1
9.1%
361.67 1
9.1%
118.54 1
9.1%

Vat
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2834.9182
Minimum9.55
Maximum18095.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.483513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.55
5-th percentile98.65
Q1339.825
median816.1
Q32511.2
95-th percentile11392
Maximum18095.6
Range18086.05
Interquartile range (IQR)2171.375

Descriptive statistics

Standard deviation5293.7954
Coefficient of variation (CV)1.8673538
Kurtosis8.5559501
Mean2834.9182
Median Absolute Deviation (MAD)484.95
Skewness2.850109
Sum31184.1
Variance28024270
MonotonicityNot monotonic
2023-04-17T19:54:30.546010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
331.15 1
9.1%
348.5 1
9.1%
9.55 1
9.1%
4688.4 1
9.1%
1063.3 1
9.1%
3952.35 1
9.1%
18095.6 1
9.1%
1070.05 1
9.1%
187.75 1
9.1%
816.1 1
9.1%
ValueCountFrequency (%)
9.55 1
9.1%
187.75 1
9.1%
331.15 1
9.1%
348.5 1
9.1%
621.35 1
9.1%
816.1 1
9.1%
1063.3 1
9.1%
1070.05 1
9.1%
3952.35 1
9.1%
4688.4 1
9.1%
ValueCountFrequency (%)
18095.6 1
9.1%
4688.4 1
9.1%
3952.35 1
9.1%
1070.05 1
9.1%
1063.3 1
9.1%
816.1 1
9.1%
621.35 1
9.1%
348.5 1
9.1%
331.15 1
9.1%
187.75 1
9.1%

Chapter
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.818182
Minimum3
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.608511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.5
Q131
median44
Q384.5
95-th percentile91.5
Maximum96
Range93
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation32.774436
Coefficient of variation (CV)0.62051427
Kurtosis-1.5117724
Mean52.818182
Median Absolute Deviation (MAD)32
Skewness-0.12493625
Sum581
Variance1074.1636
MonotonicityNot monotonic
2023-04-17T19:54:30.671012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 1
9.1%
40 1
9.1%
84 1
9.1%
44 1
9.1%
68 1
9.1%
12 1
9.1%
23 1
9.1%
85 1
9.1%
39 1
9.1%
96 1
9.1%
ValueCountFrequency (%)
3 1
9.1%
12 1
9.1%
23 1
9.1%
39 1
9.1%
40 1
9.1%
44 1
9.1%
68 1
9.1%
84 1
9.1%
85 1
9.1%
87 1
9.1%
ValueCountFrequency (%)
96 1
9.1%
87 1
9.1%
85 1
9.1%
84 1
9.1%
68 1
9.1%
44 1
9.1%
40 1
9.1%
39 1
9.1%
23 1
9.1%
12 1
9.1%

Heading
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5301.1818
Minimum306
Maximum9608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.749138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum306
5-th percentile759.5
Q13117.5
median4418
Q38498.5
95-th percentile9158
Maximum9608
Range9302
Interquartile range (IQR)5381

Descriptive statistics

Standard deviation3283.691
Coefficient of variation (CV)0.61942621
Kurtosis-1.5142387
Mean5301.1818
Median Absolute Deviation (MAD)3205
Skewness-0.12738672
Sum58313
Variance10782626
MonotonicityNot monotonic
2023-04-17T19:54:30.811632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
306 1
9.1%
4016 1
9.1%
8468 1
9.1%
4418 1
9.1%
6812 1
9.1%
1213 1
9.1%
2309 1
9.1%
8529 1
9.1%
3926 1
9.1%
9608 1
9.1%
ValueCountFrequency (%)
306 1
9.1%
1213 1
9.1%
2309 1
9.1%
3926 1
9.1%
4016 1
9.1%
4418 1
9.1%
6812 1
9.1%
8468 1
9.1%
8529 1
9.1%
8708 1
9.1%
ValueCountFrequency (%)
9608 1
9.1%
8708 1
9.1%
8529 1
9.1%
8468 1
9.1%
6812 1
9.1%
4418 1
9.1%
4016 1
9.1%
3926 1
9.1%
2309 1
9.1%
1213 1
9.1%

Subheading
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct11
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530179.27
Minimum30639
Maximum960850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.0 B
2023-04-17T19:54:30.889833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum30639
5-th percentile75969.5
Q1311840
median441899
Q3849895
95-th percentile915840
Maximum960850
Range930211
Interquartile range (IQR)538055

Descriptive statistics

Standard deviation328367.45
Coefficient of variation (CV)0.61935173
Kurtosis-1.5140053
Mean530179.27
Median Absolute Deviation (MAD)320599
Skewness-0.12759758
Sum5831972
Variance1.0782518 × 1011
MonotonicityNot monotonic
2023-04-17T19:54:30.952330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
30639 1
9.1%
401693 1
9.1%
846880 1
9.1%
441899 1
9.1%
681291 1
9.1%
121300 1
9.1%
230990 1
9.1%
852910 1
9.1%
392690 1
9.1%
960850 1
9.1%
ValueCountFrequency (%)
30639 1
9.1%
121300 1
9.1%
230990 1
9.1%
392690 1
9.1%
401693 1
9.1%
441899 1
9.1%
681291 1
9.1%
846880 1
9.1%
852910 1
9.1%
870830 1
9.1%
ValueCountFrequency (%)
960850 1
9.1%
870830 1
9.1%
852910 1
9.1%
846880 1
9.1%
681291 1
9.1%
441899 1
9.1%
401693 1
9.1%
392690 1
9.1%
230990 1
9.1%
121300 1
9.1%

Month
Categorical

Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
August
11 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters66
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAugust
2nd rowAugust
3rd rowAugust
4th rowAugust
5th rowAugust

Common Values

ValueCountFrequency (%)
August 11
100.0%

Length

2023-04-17T19:54:31.030458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:31.233586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
august 11
100.0%

Most occurring characters

ValueCountFrequency (%)
u 22
33.3%
A 11
16.7%
g 11
16.7%
s 11
16.7%
t 11
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55
83.3%
Uppercase Letter 11
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 22
40.0%
g 11
20.0%
s 11
20.0%
t 11
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 22
33.3%
A 11
16.7%
g 11
16.7%
s 11
16.7%
t 11
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 22
33.3%
A 11
16.7%
g 11
16.7%
s 11
16.7%
t 11
16.7%

Year
Categorical

Distinct1
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size216.0 B
2021
11 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters44
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 11
100.0%

Length

2023-04-17T19:54:31.296086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T19:54:31.358578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 11
100.0%

Most occurring characters

ValueCountFrequency (%)
2 22
50.0%
0 11
25.0%
1 11
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22
50.0%
0 11
25.0%
1 11
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 22
50.0%
0 11
25.0%
1 11
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 22
50.0%
0 11
25.0%
1 11
25.0%

Interactions

2023-04-17T19:54:24.139342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.545603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.748765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.826842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.951842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.201844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.295635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.529978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.576856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.826846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.858095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.951848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.108106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.217477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.670592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.826932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.905042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.029970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.280045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.373787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.592549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.654969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.904969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.951843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.014343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.186223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.295612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.748783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.920593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.998728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.279983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.373729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.467481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.686225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.748729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.983094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.029969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.092479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.264345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.373730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.842484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.998717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.092468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.373731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.451850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.545593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.764343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.826851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.061288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.123738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.170594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.342469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.467475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.920595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.092475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.170594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.451843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.545598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.639359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.842539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.920593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.154971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.201844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.248734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.420594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.545594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:10.998729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.170592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.264344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.545592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.639349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.717476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.936217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.998761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.233172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.295594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.342487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.514349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.623720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.076844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.264368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.342468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.623717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.717480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.795600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.014389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.092479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.311222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.389349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.420599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.592471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.701844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.155040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.342468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.436220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.701842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.795671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.045613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.092545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.186234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.389421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.467481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.639438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.670604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.795594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.248719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.420667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.514348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.811233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.889342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.123766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.186233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.264345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.483105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.545593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.717468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.748728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.873740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.436299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.498723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.608104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.889431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.967476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.217483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.264357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.342543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.561230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.623729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.795594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.826844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.951859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.514342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.592467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.701920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:14.967479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.045601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.280041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.342467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.436218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.639360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.701845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.873723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.904970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:25.014358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.592468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.670604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.795611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.045593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.139343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.358135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.420600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.514343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.701895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.780061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:22.951844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.983094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:25.092545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:11.670593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:12.748725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:13.873744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:15.123717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:16.217469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:17.436317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:18.498720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:19.592471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:20.779970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:21.858100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:23.029969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-17T19:54:24.061228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-17T19:54:31.436641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Hs CodeGross WeightNet WeightQuantityNo Of PackagesCustoms Value BwpCustoms value USDInvoice Amount BWPFreight BWPVatChapterHeadingSubheadingDateImporterImporter AddressExporterDeclarantORIGIN Origin CountryOrigin CountryExport CountryPort Of EntryPlace Of DischargeHs Code DescriptionCommercial DescriptionQuantity UOMPackage TypeDeclaration Office
Hs Code1.000-0.515-0.518-0.474-0.594-0.127-0.127-0.209-0.155-0.0821.0001.0001.0000.0001.0000.3331.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.000
Gross Weight-0.5151.0000.9980.7600.5810.5510.5510.5150.2370.560-0.515-0.515-0.5150.5511.0000.5771.0000.0000.0000.0000.0000.3360.3361.0001.0000.0000.6750.336
Net Weight-0.5180.9981.0000.7520.5800.5270.5270.5450.2550.527-0.518-0.518-0.5180.5511.0000.5771.0000.0000.0000.0000.0000.3360.3361.0001.0000.0000.6750.336
Quantity-0.4740.7600.7521.0000.7870.4780.4780.050-0.1460.419-0.474-0.474-0.4740.5521.0000.4711.0000.0000.0000.0000.0000.1440.1441.0001.0000.0000.4720.144
No Of Packages-0.5940.5810.5800.7871.0000.3840.384-0.009-0.2560.288-0.594-0.594-0.5940.0001.0000.3331.0000.4710.0000.0000.0000.0000.0001.0001.0000.0000.8160.000
Customs Value Bwp-0.1270.5510.5270.4780.3841.0001.0000.118-0.5270.964-0.127-0.127-0.1270.3851.0000.4711.0000.5000.0000.0000.0000.6310.6311.0001.0000.0000.6760.631
Customs value USD-0.1270.5510.5270.4780.3841.0001.0000.118-0.5270.964-0.127-0.127-0.1270.3851.0000.4711.0000.5000.0000.0000.0000.6310.6311.0001.0000.0000.6760.631
Invoice Amount BWP-0.2090.5150.5450.050-0.0090.1180.1181.0000.6450.155-0.209-0.209-0.2090.8441.0000.7451.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.000
Freight BWP-0.1550.2370.255-0.146-0.256-0.527-0.5270.6451.000-0.400-0.155-0.155-0.1550.4361.0000.0001.0000.0000.3710.3710.3710.0000.0001.0001.0000.2910.0000.000
Vat-0.0820.5600.5270.4190.2880.9640.9640.155-0.4001.000-0.082-0.082-0.0820.3851.0000.4711.0000.5000.0000.0000.0000.6310.6311.0001.0000.0000.6760.631
Chapter1.000-0.515-0.518-0.474-0.594-0.127-0.127-0.209-0.155-0.0821.0001.0001.0000.0001.0000.3331.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.000
Heading1.000-0.515-0.518-0.474-0.594-0.127-0.127-0.209-0.155-0.0821.0001.0001.0000.0001.0000.3331.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.000
Subheading1.000-0.515-0.518-0.474-0.594-0.127-0.127-0.209-0.155-0.0821.0001.0001.0000.0001.0000.3331.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.000
Date0.0000.5510.5510.5520.0000.3850.3850.8440.4360.3850.0000.0000.0001.0001.0000.3331.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0830.000
Importer1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Importer Address0.3330.5770.5770.4710.3330.4710.4710.7450.0000.4710.3330.3330.3330.3331.0001.0001.0000.9430.4710.4710.4710.6670.6671.0001.0000.0000.6670.667
Exporter1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Declarant0.0000.0000.0000.0000.4710.5000.5000.0000.0000.5000.0000.0000.0000.0001.0000.9431.0001.0000.5000.5000.5000.7070.7071.0001.0000.0000.3310.707
ORIGIN Origin Country0.0000.0000.0000.0000.0000.0000.0000.0000.3710.0000.0000.0000.0000.0001.0000.4711.0000.5001.0001.0001.0000.8660.8661.0001.0000.0000.0000.866
Origin Country0.0000.0000.0000.0000.0000.0000.0000.0000.3710.0000.0000.0000.0000.0001.0000.4711.0000.5001.0001.0001.0000.8660.8661.0001.0000.0000.0000.866
Export Country0.0000.0000.0000.0000.0000.0000.0000.0000.3710.0000.0000.0000.0000.0001.0000.4711.0000.5001.0001.0001.0000.8660.8661.0001.0000.0000.0000.866
Port Of Entry0.0000.3360.3360.1440.0000.6310.6310.0000.0000.6310.0000.0000.0000.0001.0000.6671.0000.7070.8660.8660.8661.0001.0001.0001.0000.0000.2501.000
Place Of Discharge0.0000.3360.3360.1440.0000.6310.6310.0000.0000.6310.0000.0000.0000.0001.0000.6671.0000.7070.8660.8660.8661.0001.0001.0001.0000.0000.2501.000
Hs Code Description1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Commercial Description1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Quantity UOM0.0000.0000.0000.0000.0000.0000.0000.0000.2910.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0000.000
Package Type0.0000.6750.6750.4720.8160.6760.6760.0000.0000.6760.0000.0000.0000.0831.0000.6671.0000.3310.0000.0000.0000.2500.2501.0001.0000.0001.0000.250
Declaration Office0.0000.3360.3360.1440.0000.6310.6310.0000.0000.6310.0000.0000.0000.0001.0000.6671.0000.7070.8660.8660.8661.0001.0001.0001.0000.0000.2501.000

Missing values

2023-04-17T19:54:25.420618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T19:54:25.983101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateImporterImporter AddressExporterDeclarantCountryORIGIN Origin CountryOrigin CountryExport CountryDestination CountryPort Of EntryPlace Of DischargeHs CodeHs Code DescriptionCommercial DescriptionGross WeightGross Weight UOMNet WeightNet Weight UOMQuantityQuantity UOMNo Of PackagesPackage TypeCustoms Value BwpCustoms value USDDeclaration OfficeInvoice Amount BWPFreight BWPVatChapterHeadingSubheadingMonthYear
001-Aug-21WOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITEDP O BOX 500017WOOLWORTHSWOOLWORTHS (BOTSWANA) (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaTlokweng GateTlokweng Gate3063900Other, including flour, meals and pellets of crustaceans fit for humanOther, including flours, meals and pellets of crustaceans, fit for human16.76KGM16.76KGM30.00KGM30EACH1808.92163.66Tlokweng Gate109753.6033767.55331.15330630639August2021
104-Aug-21S.R.J. ENTERPRISES (PROPRIETARY) LIMITEDPO BOX 281TRUSEALGLOBE-TECH INVESTMENTS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaTlokweng GateTlokweng Gate40169310Identifiable as integral parts of industrial machinerySEAL20.00KGM20.00KGM20000.00KGM20000KEG2370.89214.50Tlokweng Gate2370.89118.54348.50404016401693August2021
210-Aug-21VISITION ROYAL INVESTMENT (PROPRIETARY0 LIMITEDP O BOX 550296YIWU BORZ E-COMMERCE CO LTDNOTCHABOVE (PROPRIETARY) LIMITEDBOTSWANACHINAChinaChinaBotswanaGABCONGABCON84688000Other machinery and apparatusMINI ELECTRIC WELDING MACHINE3.00KGM3.00KGM1.00EA1CARTONS46.044.17GABCON122562.9359375.329.55848468846880August2021
303-Aug-21BUILDERS TRADE DEPOT(BOTSWANA)(PTY)LTDP O BOX 70021GYPROC SAINT GTOBAINHYPER TRANSPORT (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaRamatlabama BorderpostRamatlabama Borderpost44189900Other builders' joinery and carpentry of wood, including cellular woodCORNICE5511.42KGM5510.42KGM5510.42KGM13EACH31468.002846.99Ramatlabama Borderpost158701.6410190.574688.40444418441899August2021
411-Aug-21BOTGOOD INVESTMENTS (PRPPRIETARY) LIMITEDP.O. Box 50131JUDYS PRIDE FASHIONS PTY LTDBMR AGENTS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaTlokweng GateTlokweng Gate68129100Clothing, clothing accessories, footwear and headgearOTHER40.08KGM40.08KGM40.08KGM17CARTONS7233.35654.42Tlokweng Gate7233.35361.671063.30686812681291August2021
505-Aug-21PRIMEFAST (PROPRIETARY) LIMITEDPRIVATE BAGBENDORA BOERDERYHYPER TRANSPORT (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaRamatlabama BorderpostRamatlabama Borderpost12130000Cereal straw and husks, unprepared, whether or not chopped, ground,WHEAT STRAW19611.00KGM19610.00KGM19610.00KGM52BALE,COMPRESSED26886.702432.50Ramatlabama Borderpost26886.711344.333952.35121213121300August2021
610-Aug-21CORDNEX ENTERPRISE (PROPRIETARY) LIMITEDP O BOX 19NUTRI FEEDS PTY LTDTIKULE MARKETING (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaPioneer GatePioneer Gate23099092Other Preparations of a kind used in animal feedingTUB ENERGY,PROTEIN ENERY AND TUB PHOSPHATE. NO BOBS REQUIRED34000.00KGM34000.00KGM680.00KGM680BAG123099.0011137.07Pioneer Gate123099.216154.9618095.60232309230990August2021
711-Aug-21PRIVYTEC (PROPRIETARY) LIMITEDP O BOX AD 75 ACJE-ENERGY HOLDING LTDONE NATION FREIGTHS (PROPRIETARY) LIMITEDBOTSWANAHONG KONGHong KongHong KongBotswanaSir Seretse Khama AirportSir Seretse Khama Airport85291020Other aerials for reception apparatus for television, whether or not capableMI BOX S EU3.00KGM2.69KGM10.00KGM1EACH5176.20468.30Sir Seretse Khama5176.201431.721070.05858529852910August2021
802-Aug-21LOGICAL DEMAND (PROPRIETARY) LIMITEDP O BOX 1612MAANSHAN YINGKAI INTERNATIONAL TRADING COMPANY LIMITEDLONGPOINT (PROPRIETARY) LIMITEDBOTSWANACHINAChinaChinaBotswanaGABCONGABCON39269090Other articles of plastics and articles of other materials of headings .39.01FRESHNESS PROTECTION PACKAGE132.00KGM132.00KGM132.00KGM10CARTONS474.6142.94GABCON64056.13104123.21187.75393926392690August2021
911-Aug-21HEART OF AFRICA GIFTS (PROPRIETARY) LIMITEDP.O. Box 1344OROAFRICA (PTY)LTDPINNACLE EXPRESS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaPioneer GatePioneer Gate96085000Sets of articles from two or more of the foregoing subheadingsGIFTS SETS0.04KGM0.04KGM10.00EA10EACH5720.30517.53Pioneer Gate5960.75113.67816.10969608960850August2021
DateImporterImporter AddressExporterDeclarantCountryORIGIN Origin CountryOrigin CountryExport CountryDestination CountryPort Of EntryPlace Of DischargeHs CodeHs Code DescriptionCommercial DescriptionGross WeightGross Weight UOMNet WeightNet Weight UOMQuantityQuantity UOMNo Of PackagesPackage TypeCustoms Value BwpCustoms value USDDeclaration OfficeInvoice Amount BWPFreight BWPVatChapterHeadingSubheadingMonthYear
104-Aug-21S.R.J. ENTERPRISES (PROPRIETARY) LIMITEDPO BOX 281TRUSEALGLOBE-TECH INVESTMENTS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaTlokweng GateTlokweng Gate40169310Identifiable as integral parts of industrial machinerySEAL20.00KGM20.00KGM20000.00KGM20000KEG2370.89214.50Tlokweng Gate2370.89118.54348.50404016401693August2021
210-Aug-21VISITION ROYAL INVESTMENT (PROPRIETARY0 LIMITEDP O BOX 550296YIWU BORZ E-COMMERCE CO LTDNOTCHABOVE (PROPRIETARY) LIMITEDBOTSWANACHINAChinaChinaBotswanaGABCONGABCON84688000Other machinery and apparatusMINI ELECTRIC WELDING MACHINE3.00KGM3.00KGM1.00EA1CARTONS46.044.17GABCON122562.9359375.329.55848468846880August2021
303-Aug-21BUILDERS TRADE DEPOT(BOTSWANA)(PTY)LTDP O BOX 70021GYPROC SAINT GTOBAINHYPER TRANSPORT (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaRamatlabama BorderpostRamatlabama Borderpost44189900Other builders' joinery and carpentry of wood, including cellular woodCORNICE5511.42KGM5510.42KGM5510.42KGM13EACH31468.002846.99Ramatlabama Borderpost158701.6410190.574688.40444418441899August2021
411-Aug-21BOTGOOD INVESTMENTS (PRPPRIETARY) LIMITEDP.O. Box 50131JUDYS PRIDE FASHIONS PTY LTDBMR AGENTS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaTlokweng GateTlokweng Gate68129100Clothing, clothing accessories, footwear and headgearOTHER40.08KGM40.08KGM40.08KGM17CARTONS7233.35654.42Tlokweng Gate7233.35361.671063.30686812681291August2021
505-Aug-21PRIMEFAST (PROPRIETARY) LIMITEDPRIVATE BAGBENDORA BOERDERYHYPER TRANSPORT (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaRamatlabama BorderpostRamatlabama Borderpost12130000Cereal straw and husks, unprepared, whether or not chopped, ground,WHEAT STRAW19611.00KGM19610.00KGM19610.00KGM52BALE,COMPRESSED26886.702432.50Ramatlabama Borderpost26886.711344.333952.35121213121300August2021
610-Aug-21CORDNEX ENTERPRISE (PROPRIETARY) LIMITEDP O BOX 19NUTRI FEEDS PTY LTDTIKULE MARKETING (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaPioneer GatePioneer Gate23099092Other Preparations of a kind used in animal feedingTUB ENERGY,PROTEIN ENERY AND TUB PHOSPHATE. NO BOBS REQUIRED34000.00KGM34000.00KGM680.00KGM680BAG123099.0011137.07Pioneer Gate123099.216154.9618095.60232309230990August2021
711-Aug-21PRIVYTEC (PROPRIETARY) LIMITEDP O BOX AD 75 ACJE-ENERGY HOLDING LTDONE NATION FREIGTHS (PROPRIETARY) LIMITEDBOTSWANAHONG KONGHong KongHong KongBotswanaSir Seretse Khama AirportSir Seretse Khama Airport85291020Other aerials for reception apparatus for television, whether or not capableMI BOX S EU3.00KGM2.69KGM10.00KGM1EACH5176.20468.30Sir Seretse Khama5176.201431.721070.05858529852910August2021
802-Aug-21LOGICAL DEMAND (PROPRIETARY) LIMITEDP O BOX 1612MAANSHAN YINGKAI INTERNATIONAL TRADING COMPANY LIMITEDLONGPOINT (PROPRIETARY) LIMITEDBOTSWANACHINAChinaChinaBotswanaGABCONGABCON39269090Other articles of plastics and articles of other materials of headings .39.01FRESHNESS PROTECTION PACKAGE132.00KGM132.00KGM132.00KGM10CARTONS474.6142.94GABCON64056.13104123.21187.75393926392690August2021
911-Aug-21HEART OF AFRICA GIFTS (PROPRIETARY) LIMITEDP.O. Box 1344OROAFRICA (PTY)LTDPINNACLE EXPRESS (PROPRIETARY) LIMITEDBOTSWANASOUTH AFRICASouth AfricaSouth AfricaBotswanaPioneer GatePioneer Gate96085000Sets of articles from two or more of the foregoing subheadingsGIFTS SETS0.04KGM0.04KGM10.00EA10EACH5720.30517.53Pioneer Gate5960.75113.67816.10969608960850August2021
1004-Aug-21WIREY INVESTMENTS (PROPRIETARY LIMITEDP O BOX 550296YIWUBORZ E-COMMERCE CO LTDNOTCHABOVE (PROPRIETARY) LIMITEDBOTSWANACHINAChinaChinaBotswanaGABCONGABCON87083003Disc brake pads, mountedBRAKE DISC,BRAKE PADS980.00KGM980.00KGM300.00KGM20CARTONS2226.12201.40GABCON119681.6983031.66621.35878708870830August2021